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Record W2784595296 · doi:10.11575/prism/5239

PV Integration and Peak Load Shaving Using Energy Storage Feasibility Study

2017· dissertation· en· W2784595296 on OpenAlexaboutno aff
Arafat Hamed Juha

Bibliographic record

VenueOpen MIND · 2017
Typedissertation
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsPeaking power plantEnergy storagePeak loadEnvironmental scienceComputer scienceAutomotive engineeringNuclear engineeringReliability engineeringElectrical engineeringEngineeringRenewable energyDistributed generationPhysicsPower (physics)

Abstract

fetched live from OpenAlex

Energy storage system (ESS) is a growing technology in distribution systems. ESS is used in different application such as peak load shaving, system upgrade deferrals, and improving renewable energy integration. A photovoltaic system (PV) is wildly used in the distribution system, and it has given good results in reducing the amount of consumed electrical power from the grid during the daytime. Yet still, it may cause some problems for the network. The intermittency problem because the generation of power is related to weather conditions, another issue is voltage fluctuation especially with high penetration of photovoltaic systems in the network. using energy storage device along with PV system to mitigate such problems. In this thesis an investigation of the impact of installing a photovoltaic system to the part of the University of Calgary distribution network. Utilizing a properly sized energy storage system with PV to manage peak load on the university campus.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.069
GPT teacher head0.353
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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